MOT ADHD: Behavioral data analysis
Ben Cowley (adapted from Sami Karadeniz)
Setup
Preprocessing
Pruning
Discarding subjects before preprocessing. First, we use the following criteria for choosing the subjects to be preprocessed in the first place:
- subjects have been measured with the photodetector
- subjects have the full number of usable sets (= 4)
- subjects do not have too many trial-rejections
Check trial-RTs
Let’s take a look at the reaction time distribution for each subject.
If we believe that the RT distribution tails reflect theoretically invalid processes, we could consider trimming them, i.e. discard trials whose RT’s are above or below some theoretical limit.
- less than 180 ms (which is mean minus 2SD, based on Table 2 from Woods, et al., 2015 Front Hum Neurosci. 2015; 9:131)
- above median + 5 times the median absolute deviation (but we have no theoretical justification for this)
However, we actually have no strong motivation for this approach, since
- it is not clear where the threshold should be
- we aim to use parametric test statistics (based on fitting ex-gaussian distribution), for which it is better to have the original data for fitting and rely on the robustness of the statistics to account for outliers.
Still, we visualise how many trials would be rejected based on such thresholds
## `summarise()` ungrouping output (override with `.groups` argument)
Results
Results are given for each task x distractor state combination at the group and single subject levels. We will look at:
Speed
- reaction time mean, estimated from ex-gaussian fit (mu of the gaussian part)
- reaction time variability, estimated from ex-gaussian fit (sigma of the gaussian part)
- reaction time slowing, estimated from long-tail of ex-gaussian fit (tau)
- (RT median and RT variance will be included for comparison, but not for principle analysis)
Accuracy
- hit rates
- false alarm rates
- distractor effects*
*Distractor effects are calculated in the following way:
\[\frac{HR_{distractors \ absent} - HR_{distractors \ present}} {HR_{distractors \ absent} + HR_{distractors \ present}}\]
## `summarise()` regrouping output by 'Group', 'dom_resp', 'ID', 'Task' (override with `.groups` argument)
## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)
## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)
## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)
MU
Ex-Gaussian stats of Reaction time - mu (gaussian central estimate)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$mu and exgauss[Group == "Control" & dom_resp == TRUE, ]$mu
## D = 0.23333, p-value = 0.254
## alternative hypothesis: two-sidedStatistics for RT ex-gauss mu
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 2.95e+00 9.74e-02 8.33e-02
## 3 Task 2 54 8.90e-01 4.17e-01 2.58e-03
## 5 Distractors 1 27 5.47e+01 5.85e-08 * 7.37e-02
## 4 Group:Task 2 54 2.44e-01 7.84e-01 7.10e-04
## 6 Group:Distractors 1 27 1.86e-06 9.99e-01 2.69e-09
## 7 Task:Distractors 2 54 8.47e+00 6.32e-04 * 1.55e-02
## 8 Group:Task:Distractors 2 54 1.47e+00 2.38e-01 2.74e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.861 0.1435
## 4 Group:Task 0.861 0.1435
## 7 Task:Distractors 0.808 0.0623
## 8 Group:Task:Distractors 0.808 0.0623
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF] p[HF]<.05
## 3 Task 0.878 0.40549 0.934 0.41087
## 4 Group:Task 0.878 0.75568 0.934 0.76943
## 7 Task:Distractors 0.839 0.00137 * 0.888 0.00108 *
## 8 Group:Task:Distractors 0.839 0.24002 0.888 0.23967
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.3497415
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.02739484 0.25083877
## Deg. of Freedom 1 27
##
## Residual standard error: 0.09638633
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000771667 0.000214281 0.023690890
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.02094565
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.024009408 0.000000001 0.011826235
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.02092866
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.004629319 0.000827573 0.015172424
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01676218
## Estimated effects may be unbalanced## `geom_smooth()` using formula 'y ~ x'
Constrasts for exgauss mu
MU by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 2.949 0.0974
## Distractors 1 27 54.749 <.0001
## Task 2 54 0.890 0.4167
## Group:Distractors 1 27 0.000 0.9989
## Group:Task 2 54 0.244 0.7842
## Distractors:Task 2 54 8.470 0.0006
## Group:Distractors:Task 2 54 1.473 0.2384## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 26.452 <.0001
## Task 2 54 0.711 0.4955
## Distractors:Task 2 54 8.220 0.0008
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 28.363 <.0001
## Task 2 54 0.412 0.6643
## Distractors:Task 2 54 1.491 0.2343## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 1.054 0.3111
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 3.224 0.0804
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 2.976 0.0925
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 1.838 0.1831
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 3.795 0.0587
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 2.392 0.1301## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 29.54 2.817 0.1038
## Task 2 103.05 6.109 0.0031
## Group:Task 2 103.05 1.196 0.3066
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 29.54 2.815 0.1039
## Task 2 103.05 1.589 0.2090
## Group:Task 2 103.05 0.252 0.7779## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 41.507 <.0001
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 3.787 0.0553
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 3.324 0.0722
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 23.222 <.0001
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 8.426 0.0048
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 8.137 0.0056## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 2.187 0.1489
## Distractors 1 77.19 63.708 <.0001
## Group:Distractors 1 77.19 1.652 0.2025
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 2.606 0.1162
## Distractors 1 77.19 11.672 0.0010
## Group:Distractors 1 77.19 0.381 0.5389
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 3.353 0.0763
## Distractors 1 77.19 10.845 0.0015
## Group:Distractors 1 77.19 0.450 0.5043## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 5.965 0.0035
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 1.320 0.2715
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 1.175 0.3130
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 0.492 0.6128
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001982
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2092 1 160 0.648## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.004249
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.9617 1 160 0.3282## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002267
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2738 1 160 0.6015## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0235
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 33.09 1 160 4.362e-08## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.006471
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.23 1 160 0.1373## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.00311
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5152 1 160 0.4739## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003361
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6016 1 160 0.4391## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005715
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.623 1 160 0.2045## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003626
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6538 1 160 0.42## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002088
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2168 1 160 0.6422## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.02349
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 30.86 1 160 1.134e-07## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01573
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 12.3 1 160 0.0005895## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.007545
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.83 1 160 0.09447## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.008181
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.328 1 160 0.06999## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004628
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.203 1 160 0.1397## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002217
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5057 1 160 0.478## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.00241
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5976 1 160 0.4406SIGMA
Ex-Gaussian stats of Reaction time - sigma (gaussian dispersion)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$sigma and exgauss[Group == "Control" & dom_resp == TRUE, ]$sigma
## D = 0.2381, p-value = 0.2342
## alternative hypothesis: two-sidedStatistics for RTV - exgauss sigma:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 4.372677168 0.04606248 * 9.189650e-02
## 3 Task 2 54 1.249324307 0.29485187 8.298936e-03
## 5 Distractors 1 27 5.795958889 0.02316748 * 2.348284e-02
## 4 Group:Task 2 54 0.138527943 0.87094732 9.270455e-04
## 6 Group:Distractors 1 27 0.001054741 0.97433074 4.376120e-06
## 7 Task:Distractors 2 54 2.065145049 0.13669867 6.252890e-03
## 8 Group:Task:Distractors 2 54 1.097591422 0.34100137 3.333075e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9994394 0.9927361
## 4 Group:Task 0.9994394 0.9927361
## 7 Task:Distractors 0.9754472 0.7238507
## 8 Group:Task:Distractors 0.9754472 0.7238507
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9994397 0.2948431 1.079324 0.2948519
## 4 Group:Task 0.9994397 0.8708397 1.079324 0.8709473
## 7 Task:Distractors 0.9760356 0.1380059 1.051145 0.1366987
## 8 Group:Task:Distractors 0.9760356 0.3399814 1.051145 0.3410014
## p[HF]<.05
## 3
## 4
## 7
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.05401102
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00733327 0.04528077
## Deg. of Freedom 1 27
##
## Residual standard error: 0.04095199
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000604827 0.000067242 0.013105816
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01557884
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.001743079 0.000000317 0.008117886
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.01733962
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.000434113 0.000242342 0.005961450
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01050701
## Estimated effects may be unbalanced## `geom_smooth()` using formula 'y ~ x'
Constrasts for exgauss sigma
SIGMA by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 4.373 0.0461
## Distractors 1 27 5.796 0.0232
## Task 2 54 1.249 0.2949
## Group:Distractors 1 27 0.001 0.9743
## Group:Task 2 54 0.139 0.8709
## Distractors:Task 2 54 2.065 0.1367
## Group:Distractors:Task 2 54 1.098 0.3410## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 2.878 0.1013
## Task 2 54 0.740 0.4819
## Distractors:Task 2 54 2.967 0.0599
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 2.921 0.0989
## Task 2 54 0.645 0.5289
## Distractors:Task 2 54 0.096 0.9083## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 1.248 0.2682
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.749 0.0573
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.754 0.0571
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.293 0.0743
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.503 0.0658
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 1.539 0.2193## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 36.38 3.659 0.0637
## Task 2 94.70 2.956 0.0569
## Group:Task 2 94.70 0.527 0.5922
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 36.38 3.757 0.0604
## Task 2 94.70 0.053 0.9485
## Group:Task 2 94.70 0.350 0.7057## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 7.941 0.0064
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 0.779 0.3806
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 0.027 0.8707
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 2.494 0.1192
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 1.242 0.2693
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 1.439 0.2346## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 2.892 0.0962
## Distractors 1 63.97 9.758 0.0027
## Group:Distractors 1 63.97 0.864 0.3561
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 4.369 0.0425
## Distractors 1 63.97 1.986 0.1636
## Group:Distractors 1 63.97 0.019 0.8896
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 3.005 0.0901
## Distractors 1 63.97 0.905 0.3451
## Group:Distractors 1 63.97 0.513 0.4766## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 2.797 0.0660
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.076 0.9271
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.610 0.5453
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.336 0.7154
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002862
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.8135 1 160 0.3685## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0009336
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.08657 1 160 0.769## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003795
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.431 1 160 0.2334## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.006248
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.361 1 160 0.03835## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001016
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1025 1 160 0.7493## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0006619
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.04352 1 160 0.835## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0003538
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.01243 1 160 0.9114## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004352
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.755 1 160 0.1871## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001698
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2672 1 160 0.6059## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002654
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6529 1 160 0.4203## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.006419
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.296 1 160 0.0398## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005718
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.03 1 160 0.08364## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001515
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2126 1 160 0.6453## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.004203
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.637 1 160 0.2025## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002351
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.06 1 160 0.3048## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0004263
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.03486 1 160 0.8521## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001925
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.7104 1 160 0.4006TAU
Ex-Gaussian stats of Reaction time - tau (exponential tail)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$tau and exgauss[Group == "Control" & dom_resp == TRUE, ]$tau
## D = 0.1619, p-value = 0.6877
## alternative hypothesis: two-sidedStatistics for RT ex-gauss tau:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.07631259 0.7844617 0.0010830259
## 3 Task 2 54 1.39517295 0.2565781 0.0089559367
## 5 Distractors 1 27 0.07716397 0.7832942 0.0004362719
## 4 Group:Task 2 54 0.62658427 0.5382542 0.0040421317
## 6 Group:Distractors 1 27 0.48632417 0.4915334 0.0027432474
## 7 Task:Distractors 2 54 0.80645202 0.4517426 0.0085521564
## 8 Group:Task:Distractors 2 54 0.37485044 0.6891666 0.0039934426
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9418604 0.4590133
## 4 Group:Task 0.9418604 0.4590133
## 7 Task:Distractors 0.9659766 0.6376254
## 8 Group:Task:Distractors 0.9659766 0.6376254
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9450549 0.2567726 1.014004 0.2565781
## 4 Group:Task 0.9450549 0.5297651 1.014004 0.5382542
## 7 Task:Distractors 0.9670961 0.4482268 1.040409 0.4517426
## 8 Group:Task:Distractors 0.9670961 0.6822427 1.040409 0.6891666
## p[HF]<.05
## 3
## 4
## 7
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.08739718
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.000059174 0.020936263
## Deg. of Freedom 1 27
##
## Residual standard error: 0.02784631
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000508445 0.000221509 0.009544999
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01329508
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.000019899 0.000150134 0.008335238
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.01757022
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.000493742 0.000218830 0.015762064
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01708479
## Estimated effects may be unbalanced## boundary (singular) fit: see ?isSingular## `geom_smooth()` using formula 'y ~ x'
Constrasts for exgauss tau
TAU by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 0.076 0.7845
## Distractors 1 27 0.077 0.7833
## Task 2 54 1.052 0.3561
## Group:Distractors 1 27 0.486 0.4915
## Group:Task 2 54 0.473 0.6259
## Distractors:Task 2 54 1.005 0.3729
## Group:Distractors:Task 2 54 0.467 0.6294## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 0.460 0.5036
## Task 2 54 0.308 0.7364
## Distractors:Task 2 54 0.050 0.9512
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 0.091 0.7650
## Task 2 54 1.250 0.2947
## Distractors:Task 2 54 1.470 0.2389## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.780 0.3788
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.202 0.6540
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.023 0.8796
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.184 0.6690
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.534 0.4663
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.205 0.6514## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 45.56 0.019 0.8905
## Task 2 108.00 1.742 0.1801
## Group:Task 2 108.00 0.939 0.3941
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 45.56 0.367 0.5477
## Task 2 108.00 0.315 0.7303
## Group:Task 2 108.00 0.000 0.9998## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.040 0.8417
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.211 0.6476
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.388 0.5354
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 1.793 0.1843
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.039 0.8431
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.935 0.3365## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.077 0.7831
## Distractors 1 79.54 0.618 0.4340
## Group:Distractors 1 79.54 1.155 0.2858
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.031 0.8606
## Distractors 1 79.54 0.037 0.8482
## Group:Distractors 1 79.54 0.219 0.6411
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.569 0.4536
## Distractors 1 79.54 1.254 0.2663
## Group:Distractors 1 79.54 0.050 0.8232## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.197 0.8214
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.161 0.8517
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 2.566 0.0815
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.154 0.8570
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005404
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.343 1 160 0.1278## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002444
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.4795 1 160 0.4897## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002959
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.7028 1 160 0.4031## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001118
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1129 1 160 0.7373## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005065
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.059 1 160 0.1533## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -3.676e-05
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0001085 1 160 0.9917## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.005102
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.089 1 160 0.1503## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001433
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1538 1 160 0.6954## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002779
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5785 1 160 0.448## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001346
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1357 1 160 0.7131## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002599
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5694 1 160 0.4516## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001035
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0803 1 160 0.7773## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0001441
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.001556 1 160 0.9686## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0008913
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0595 1 160 0.8076## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002015
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6291 1 160 0.4289## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 9.045e-05
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.001268 1 160 0.9716## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002105
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6868 1 160 0.4085Hit rates
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: hit_rates[Group == "Control" & dom_resp == FALSE, ]$hit_rate and hit_rates[Group == "Control" & dom_resp == TRUE, ]$hit_rate
## D = 0.48095, p-value = 0.0006095
## alternative hypothesis: two-sidedStatistics for hit rates
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 1.184201e-04 9.913975e-01 3.503582e-06
## 3 Task 2 54 4.150472e-01 6.623975e-01 1.069931e-03
## 5 Distractors 1 27 7.184181e+01 4.342724e-09 * 1.416731e-01
## 4 Group:Task 2 54 4.748406e-02 9.536654e-01 1.225230e-04
## 6 Group:Distractors 1 27 2.126028e-03 9.635628e-01 4.884545e-06
## 7 Task:Distractors 2 54 3.916128e+00 2.581213e-02 * 9.974818e-03
## 8 Group:Task:Distractors 2 54 2.198191e+00 1.208401e-01 5.623646e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9639195 0.6201978
## 4 Group:Task 0.9639195 0.6201978
## 7 Task:Distractors 0.9009637 0.2577478
## 8 Group:Task:Distractors 0.9009637 0.2577478
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9651760 0.65531325 1.0381051 0.66239749
## 4 Group:Task 0.9651760 0.94946032 1.0381051 0.95366544
## 7 Task:Distractors 0.9098881 0.02989337 * 0.9720671 0.02701283
## 8 Group:Task:Distractors 0.9098881 0.12613623 0.9720671 0.12246783
## p[HF]<.05
## 3
## 4
## 7 *
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.8621807
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.0000043 0.9758254
## Deg. of Freedom 1 27
##
## Residual standard error: 0.1901097
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.00132633 0.00014969 0.08511514
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03970147
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.20179408 0.00000597 0.07577765
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.05297716
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.01173242 0.00690856 0.08485660
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03964112
## Estimated effects may be unbalanced## `geom_smooth()` using formula 'y ~ x'
Constrasts for Hit Rates
Hit rate by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 0.000 0.9914
## Distractors 1 27 71.837 <.0001
## Task 2 54 0.416 0.6620
## Group:Distractors 1 27 0.002 0.9636
## Group:Task 2 54 0.048 0.9536
## Distractors:Task 2 54 3.910 0.0259
## Group:Distractors:Task 2 54 2.195 0.1212## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 35.100 <.0001
## Task 2 54 0.152 0.8595
## Distractors:Task 2 54 5.612 0.0061
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 36.797 <.0001
## Task 2 54 0.317 0.7296
## Distractors:Task 2 54 0.311 0.7342## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.215 0.6454
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.393 0.5344
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.019 0.8913
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.015 0.9022
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.103 0.7500
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.193 0.6627## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 31.17 0.000 0.9985
## Task 2 108.00 3.318 0.0399
## Group:Task 2 108.00 0.806 0.4492
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 31.17 0.001 0.9819
## Task 2 108.00 1.008 0.3684
## Group:Task 2 108.00 1.436 0.2424## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 42.230 <.0001
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 8.918 0.0038
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 7.388 0.0082
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 20.833 <.0001
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 13.026 0.0006
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 18.672 <.0001## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.008 0.9308
## Distractors 1 74.60 61.543 <.0001
## Group:Distractors 1 74.60 2.257 0.1372
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.000 0.9941
## Distractors 1 74.60 21.672 <.0001
## Group:Distractors 1 74.60 0.130 0.7199
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.004 0.9497
## Distractors 1 74.60 24.575 <.0001
## Group:Distractors 1 74.60 1.097 0.2983## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 3.439 0.0356
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 2.324 0.1027
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.587 0.5578
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.041 0.9600
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.006781
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5052 1 160 0.4783## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005105
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2863 1 160 0.5933## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001676
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.03086 1 160 0.8608## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.06775
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 56.74 1 160 3.451e-12## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.004873
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2609 1 160 0.6102## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.00678
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5051 1 160 0.4783## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001907
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.03996 1 160 0.8418## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003306
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1121 1 160 0.7382## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004971
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2534 1 160 0.6154## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001665
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.02843 1 160 0.8663## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.06849
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 54.12 1 160 9.275e-12## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0307
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 9.664 1 160 0.002225## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01366
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.912 1 160 0.1686## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01704
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.978 1 160 0.08632## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.01291
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.537 1 160 0.06182## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.003438
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2508 1 160 0.6172## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.009475
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.905 1 160 0.1695False alarms
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: fa_rates[Group == "Control" & dom_resp == FALSE, ]$fa_rate and fa_rates[Group == "Control" & dom_resp == TRUE, ]$fa_rate
## D = 0.16667, p-value = 0.7159
## alternative hypothesis: two-sidedStatistics for false alarm rates:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.99624544 0.3270825057 0.0235676226
## 3 Task 2 54 1.47716918 0.2373600463 0.0082416656
## 5 Distractors 1 27 15.43837908 0.0005336195 * 0.0529696664
## 4 Group:Task 2 54 0.72569044 0.4886497069 0.0040659392
## 6 Group:Distractors 1 27 0.03775998 0.8473805867 0.0001367836
## 7 Task:Distractors 2 54 9.37433253 0.0003201517 * 0.0323030917
## 8 Group:Task:Distractors 2 54 0.04387023 0.9571122216 0.0001561948
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9653826 0.6325478
## 4 Group:Task 0.9653826 0.6325478
## 7 Task:Distractors 0.9583853 0.5754691
## 8 Group:Task:Distractors 0.9583853 0.5754691
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9665409 0.2378270702 1.039743 0.2373600463
## 4 Group:Task 0.9665409 0.4843929958 1.039743 0.4886497069
## 7 Task:Distractors 0.9600479 0.0003975696 * 1.031955 0.0003201517
## 8 Group:Task:Distractors 0.9600479 0.9524710628 1.031955 0.9571122216
## p[HF]<.05
## 3
## 4
## 7 *
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.9771982
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00274685 0.07444458
## Deg. of Freedom 1 27
##
## Residual standard error: 0.05250911
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000904047 0.000464614 0.017286406
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01789186
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.006351256 0.000015569 0.011132366
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.02030541
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.003792034 0.000017779 0.010941836
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.0142347
## Estimated effects may be unbalanced## `geom_smooth()` using formula 'y ~ x'
Constrasts for False Alarms
False Alarm by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 0.996 0.3271
## Distractors 1 27 15.438 0.0005
## Task 2 54 1.477 0.2374
## Group:Distractors 1 27 0.038 0.8474
## Group:Task 2 54 0.726 0.4887
## Distractors:Task 2 54 9.374 0.0003
## Group:Distractors:Task 2 54 0.044 0.9571## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 8.218 0.0079
## Task 2 54 2.013 0.1435
## Distractors:Task 2 54 4.949 0.0106
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 7.224 0.0122
## Task 2 54 0.125 0.8831
## Distractors:Task 2 54 4.452 0.0162## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.587 0.4467
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.218 0.6425
##
## Distractors = Absent, Task = AttendNone:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 1.615 0.2087
##
## Distractors = Present, Task = AttendNone:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 1.632 0.2063
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.322 0.5727
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.245 0.6222## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 34.90 1.002 0.3237
## Task 2 102.81 1.133 0.3260
## Group:Task 2 102.81 0.354 0.7030
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 34.90 0.741 0.3952
## Task 2 102.81 7.943 0.0006
## Group:Task 2 102.81 0.569 0.5677## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 9.535 0.0029
##
## Task = AttendNone, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 0.032 0.8590
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 10.227 0.0021
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 7.327 0.0085
##
## Task = AttendNone, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 0.030 0.8628
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 10.193 0.0021## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 0.471 0.4964
## Distractors 1 71.58 16.822 0.0001
## Group:Distractors 1 71.58 0.116 0.7348
##
## Task = AttendNone:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 2.014 0.1636
## Distractors 1 71.58 0.062 0.8043
## Group:Distractors 1 71.58 0.000 0.9938
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 0.350 0.5573
## Distractors 1 71.58 20.414 <.0001
## Group:Distractors 1 71.58 0.007 0.9351## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 0.185 0.8315
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 6.117 0.0031
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 1.342 0.2659
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 2.262 0.1093
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [3] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [5] "Control.Absent.AttendNone" "ADHD.Absent.AttendNone"
## [7] "Control.Present.AttendNone" "ADHD.Present.AttendNone"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"## TEST CONTRAST: NvLR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001049
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.07544 1 160 0.7839## TEST CONTRAST: LvNR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0009479
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.06164 1 160 0.8042## TEST CONTRAST: RvNL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001997
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2735 1 160 0.6017## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01151
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 10.22 1 160 0.001678## TEST CONTRAST: drAN_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.009413
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 6.078 1 160 0.01474## TEST CONTRAST: drAL_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.003608
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.8931 1 160 0.3461## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005805
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.311 1 160 0.1304## TEST CONTRAST: NvLR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.007948
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.044 1 160 0.04601## TEST CONTRAST: LvNR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001874
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2247 1 160 0.6361## TEST CONTRAST: RvNL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.006074
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.362 1 160 0.1263## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0127
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 11.62 1 160 0.0008249## TEST CONTRAST: drAN_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.01036
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 6.873 1 160 0.009594## TEST CONTRAST: drAL_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004923
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.552 1 160 0.2147## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005438
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.893 1 160 0.1707## TEST CONTRAST: drAN_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0004741
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.02978 1 160 0.8632## TEST CONTRAST: drAL_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0006574
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.05724 1 160 0.8112## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0001832
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.004446 1 160 0.9469Distractor effect
## `summarise()` regrouping output by 'Group' (override with `.groups` argument)
Statistics for distractor effect:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.000181595 0.9893473 3.337908e-06
## 3 Task 2 54 3.703857225 0.0310894 * 6.463280e-02
## 4 Group:Task 2 54 2.241067418 0.1161461 4.013131e-02
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.8796146 0.1887128
## 4 Group:Task 0.8796146 0.1887128
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF] p[HF]<.05
## 3 Task 0.89255 0.0364544 * 0.9514771 0.0334055 *
## 4 Group:Task 0.89255 0.1225835 0.9514771 0.1190267
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.04157256
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00000044 0.06523006
## Deg. of Freedom 1 27
##
## Residual standard error: 0.04915209
## 2 out of 3 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.00864155 0.00549520 0.06620525
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03501461
## Estimated effects may be unbalancedContrasts for distractor effect:
## [1] "Control.AttendFull" "ADHD.AttendFull" "Control.AttendLeft"
## [4] "ADHD.AttendLeft" "Control.AttendRight" "ADHD.AttendRight"##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## DE_group == 0 0.0004263
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0001816 1 79 0.9893##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_ctrl == 0 0.03795
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 10.96 1 79 0.001403##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## RvFL_ctrl == 0 -0.02008
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.069 1 79 0.0837## TEST CONTRAST: FvLR ctrl V ADHD 0.5 -0.5 -0.25 0.25 -0.25 0.25##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_adhd == 0 0.01642
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.245 1 79 0.04265DE summary
There was no effect of group on Distractor effect. Task = attend-Full differed strongly from Left/Right for CTRL group (F = 11), and this induced a between groups difference with ADHD in the same Task contrast (F = 4.2)